Industrial internet identification data element analysis method and system

Abstract

The invention relates to the technical field of data analysis, and discloses an industrial internet identification data element analysis system, which comprises a core layer, the core layer is connected with an analysis layer, an intelligent decision-making layer, a data feature layer and a support layer, and the core layer comprises a platform interface, a calculation engine and a data raw material; the invention also provides an industrial internet identification data element analysis method, which comprises the following steps: S1, establishing a support system layer, constructing a block chain infrastructure and constructing an identification data element registration platform; s2, building a data feature layer, wherein the data feature layer comprises the steps of adding a data identifier, cleaning data and building a scene data model; according to the method, through connection with the identification main database, multi-link, multi-industry and multi-region identification data integrated in the main database, cooperation and information sharing of all parties of an industry chain are promoted, and resource optimization and synergistic interaction of a value chain are achieved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and specifically to an industrial Internet identification data element analysis method and system. Background Art

[0002] Data analysis is not just about counting industrial Internet data, but also requires the integration of multiple artificial intelligence technologies, such as natural language processing, image recognition, speech recognition, etc., to provide a more intelligent multi-mode solution for industrial Internet data analysis.

[0003] At present, the main implementation principles of the industrial Internet data processing and analysis methods and systems are as follows: 1. Real-time collection of various industrial Internet data (including industrial equipment, sensors, control systems, etc.); 2. Through a series of data preprocessing operations such as data cleaning, data deduplication, data correction, and data conversion, ensure that the data input into the system module is of high quality and reliability; 3. Utilize distributed file systems and database cluster technologies to achieve fast data storage, efficient query and elastic expansion through data indexing, caching and compression; 4. Use a variety of analysis methods including statistical analysis, association rule mining, cluster analysis, time series analysis, etc. to conduct in-depth mining and analysis of data to meet different application scenarios; 5. Through data visualization technology, including charts, maps, reports, etc., display data analysis results in an intuitive and vivid way.

[0004] However, although the above technologies can realize the analysis and visualization of the industrial Internet and meet a certain degree of scenario application through protocol conversion, they are not connected with the industrial Internet identification data system and data base, and lack the ability to support cross-industry and cross-subject. Due to the high cost of cross-industry and cross-subject application data integration adaptation, including the lack of uniformity in data format or data dictionary, the application integration of cross-industry and cross-subject industrial software requires the development of targeted middleware for data format conversion, which will bring a lot of additional costs to data integration; in addition, the underlying logic, data format and upper-level application mode of different application scenarios are quite different; in the complete industrial process, it is very easy to cause the upper layers of data, instructions and information transmission to be blocked, and it is difficult to effectively connect product life cycle information data, resulting in the formation of "information islands" in various business links of enterprises, which ultimately affects the integration and interconnection of overall informatization. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the shortcomings of the prior art, the present invention provides an industrial Internet identification data element analysis method and system, which mainly solves the pain points of enterprises in data processing, production management, equipment maintenance, decision support and industrial chain collaboration. By connecting with the industrial Internet identification master database, the industrial Internet identification data system is connected. Based on the identification resolution integration and promotion Paas platform provided middleware data docking capabilities, the interconnection of data and software across platforms and industries is solved, the R&D cost is reduced, and the data element access, integration and cleaning of equipment, process, R&D, production, quality, energy consumption and other links and processes are realized; data analysis and statistical capabilities are provided through data element identification, model selection, model training, model testing and other processes.

[0007] (II) Technical solution

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] An industrial Internet identification data element analysis system comprises a core layer, wherein the core layer is connected to an analysis layer, an intelligent decision-making layer, a data feature layer, and a support layer. The core layer comprises a platform interface, a computing engine, and data raw materials, and the platform interface comprises an HTTP or REST interface, a TCP or Socket or RPC interface, a JSON or XML interface, the computing engine comprises a batch computing engine, a streaming computing engine, and a streaming-batch integrated engine, the data raw materials comprise data elements, data spaces, log data, dimensional data, aggregated data, and factual data, the intelligent decision-making layer comprises industrial chain operation monitoring, industrial chain maps, industrial public opinion capture, industrial early warning analysis, and industrial decision-making assistance, the analysis layer comprises subject management and analysis modules, the subject management comprises data resource holders, data processing users, and data product operators, the data feature layer comprises industries, regions, industrial chains, supply chains, and processes, the support layer comprises a blockchain infrastructure, and the blockchain infrastructure comprises account book maintenance, a consensus mechanism, smart contracts, and evidence storage services.

[0010] Furthermore, the core layer also includes security governance, which includes metadata management, data desensitization, data privacy and data permissions.

[0011] Based on the above-mentioned scheme, the analysis module includes multi-scenario data statistical analysis, multi-industry data statistical analysis, multi-regional data statistical analysis, industrial chain data access analysis, industrial chain operation monitoring, industrial chain effectiveness diagnosis, supply chain element analysis, and industrial chain data application analysis.

[0012] As a further solution of the present invention, the data feature layer includes a scenario module, which includes scenarios in production and processing, equipment operation and maintenance, environmental monitoring and production safety.

[0013] On the basis of the above-mentioned scheme, the support layer includes an identification data element registration and transaction platform, and this platform includes data storage, data registration, data transaction and subject management.

[0014] The present invention also proposes an industrial Internet identification data element analysis method, comprising the following steps:

[0015] S1: Establish a support system layer, build blockchain infrastructure and set up an identification data element registration platform. The construction of blockchain infrastructure provides industrial Internet identification data element analysis methods and systems based on account maintenance, consensus mechanism, smart contracts and encrypted evidence services. The identification data element registration platform includes four main modules: data evidence, data registration, data transaction and subject management, to establish the credibility and value of data;

[0016] S2: Building a data feature layer, which includes: adding data identification, data cleaning, and establishing a scenario data model;

[0017] S201: Add data identification, by uniquely identifying and describing various data for cross-industry collaborative applications such as industry data, regional data, industrial chain data, supply chain data, and process data, and by assigning a unique identifier to each data entity, so as to identify and track the data throughout the entire data management and use process;

[0018] S202: Data cleaning: cleaning data through data screening and filtering, data standardization and format unification, missing value processing, outlier processing, data integration, grouping and aggregation;

[0019] S203: Establishing a scenario data model, which includes a production and processing data model, an equipment operation and maintenance data model, an environmental monitoring data model, and a production safety data model;

[0020] S3: Build the core layer, first build the data raw materials, then build the computing engine, then build the data analysis layer, and finally build the intelligent decision-making layer.

[0021] As a further solution of the present invention, the production and processing process data model mainly defines key data fields such as production procedures, process parameters, production indicators, including timestamp, product information, production status, and process parameters; the equipment operation and maintenance data model defines key data fields such as equipment information, equipment status, operating parameters, and maintenance records, including equipment ID, equipment type, operating status, and maintenance cycle; the environmental monitoring data model defines key data fields such as environmental monitoring points, environmental parameters, and monitoring time, including indicators of air quality, water quality monitoring, noise monitoring, and temperature and humidity; the production safety data model defines key data fields such as safety events, accident records, and safety inspections, including accident type, accident location, accident cause, and safety inspection results.

[0022] Furthermore, the data raw materials for construction, i.e., the original data used in the data element analysis system, include data elements, data space, log data, dimensional data, composite data, and real-time data; the calculation engine for construction includes three engine calculation methods: batch calculation engine, streaming calculation engine, and integrated batch and stream calculation engine.

[0023] On the basis of the above-mentioned plan, the data analysis layer is constructed to create various application scenarios for different industries such as ships, automobiles, engineering machinery, medicine, food, daily chemicals, and electronic information, and develop functions such as production process analysis, quality management and defect analysis, industrial chain operation monitoring, industrial chain effectiveness diagnosis, and supply chain element analysis. The intelligent decision-making layer is constructed to build a SaaS application system for industrial chain operation monitoring, industrial chain mapping, industrial public opinion capture, industrial early warning analysis, and industrial decision-making assistance.

[0024] (III) Beneficial effects

[0025] Compared with the prior art, the present invention provides an industrial Internet identification data element analysis method and system, which has the following beneficial effects:

[0026] 1. The present invention can collect, integrate and clean equipment data and business data of equipment, technology, research and development, production, quality, energy consumption and other links in industries such as ships, automobiles, engineering machinery, and medicine. Compared with existing technologies, it has higher real-time performance and faster collection efficiency.

[0027] 2. The present invention is connected with the main identification database, and the multi-link, multi-industry, and multi-region identification data integrated in the main database promotes collaborative cooperation and information sharing among all parties in the industrial chain, thereby achieving resource optimization and synergistic efficiency of the value chain.

[0028] 3. The present invention analyzes and mines identification data elements by applying statistics, machine learning, data mining and other technologies, extracts valuable information and knowledge, supports statistical analysis capabilities of data by industry, scenario and region, and can visualize the analysis results in an intuitive and easy-to-understand form, generate charts, reports or dashboards, and provide decision-making analysis and intelligent feedback for different scenarios such as production and processing processes, equipment operation and maintenance, and environmental monitoring, to help users better understand data and make decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a schematic diagram of the framework structure of an industrial Internet identification data element analysis system proposed in the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Example 1

[0032] Reference Figure 1 , an industrial Internet identification data element analysis system, including a core layer, the core layer is connected with an analysis layer, an intelligent decision-making layer, a data feature layer, and a support layer, the core layer includes a platform interface, a computing engine and data raw materials, and the platform interface includes an HTTP or REST interface, a TCP or Socket or RPC interface, a JSON or XML interface, the computing engine includes a batch computing engine, a streaming computing engine and a streaming-batch integrated engine, the data raw materials include data components, data space, log data, dimension data, aggregated data and factual data, the intelligent decision-making layer includes industrial chain operation monitoring, industrial chain map, industrial public opinion capture, industrial early warning analysis, and industrial decision-making assistance, the analysis layer includes subject management and analysis modules, the subject management includes data resource holders, data processing users, and data product operators, the data feature layer includes industries, regions, industrial chains, supply chains and processes, the support layer includes blockchain infrastructure, and the blockchain infrastructure includes account book maintenance, consensus mechanism, smart contracts and evidence storage services.

[0033] In the present invention, the core layer also includes security governance, which includes metadata management, data desensitization, data privacy and data permissions. The analysis module includes multi-scenario data statistical analysis, multi-industry data statistical analysis, multi-regional data statistical analysis, industrial chain data access analysis, industrial chain operation monitoring, industrial chain effectiveness diagnosis, supply chain element analysis, and industrial chain data application analysis.

[0034] The present invention also proposes an industrial Internet identification data element analysis method, comprising the following steps:

[0035] S1: Establish a support system layer, build blockchain infrastructure and set up an identification data element registration platform. The construction of blockchain infrastructure provides industrial Internet identification data element analysis methods and systems with services based on account maintenance, consensus mechanism, smart contracts and encrypted evidence storage. The blockchain consensus mechanism is used to ensure the reliability of the data element analysis system. The identification data element registration platform includes four main modules: data evidence storage, data registration, data transaction and subject management, to establish the credibility and value of data.

[0036] S2: Building a data feature layer, which includes: adding data identification, data cleaning, and establishing a scenario data model;

[0037] S201: Add data identification, by uniquely identifying and describing various data for cross-industry collaborative applications such as industry data, regional data, industrial chain data, supply chain data, and process data, and by assigning a unique identifier to each data entity, so as to identify and track the data throughout the entire data management and use process;

[0038] S202: Data cleaning: cleaning data through data screening and filtering, data standardization and format unification, missing value processing, outlier processing, data integration, grouping and aggregation;

[0039] S203: Establishing a scenario data model, which includes a production and processing data model, an equipment operation and maintenance data model, an environmental monitoring data model, and a production safety data model;

[0040] S3: Build the core layer, first build the data raw materials, then build the computing engine, then build the data analysis layer, and finally build the intelligent decision-making layer.

[0041] In particular, the production and processing data model mainly defines key data fields such as production procedures, process parameters, and production indicators, including timestamp, product information, production status, and process parameters; the equipment operation and maintenance data model defines key data fields such as equipment information, equipment status, operating parameters, and maintenance records, including equipment ID, equipment type, operating status, and maintenance cycle; the environmental monitoring data model defines key data fields such as environmental monitoring points, environmental parameters, and monitoring time, including indicators of air quality, water quality monitoring, noise monitoring, and temperature and humidity; the production safety data model defines key data fields such as safety incidents, accident records, and safety inspections, including accident type, accident location, accident cause, and safety inspection results.

[0042] Example 2

[0043] Reference Figure 1 , an industrial Internet identification data element analysis system, including a core layer, the core layer is connected with an analysis layer, an intelligent decision-making layer, a data feature layer, and a support layer, the core layer includes a platform interface, a computing engine and data raw materials, and the platform interface includes an HTTP or REST interface, a TCP or Socket or RPC interface, a JSON or XML interface, the computing engine includes a batch computing engine, a streaming computing engine and a streaming-batch integrated engine, the data raw materials include data components, data space, log data, dimension data, aggregated data and factual data, the intelligent decision-making layer includes industrial chain operation monitoring, industrial chain map, industrial public opinion capture, industrial early warning analysis, and industrial decision-making assistance, the analysis layer includes subject management and analysis modules, the subject management includes data resource holders, data processing users, and data product operators, the data feature layer includes industries, regions, industrial chains, supply chains and processes, the support layer includes blockchain infrastructure, and the blockchain infrastructure includes account book maintenance, consensus mechanism, smart contracts and evidence storage services.

[0044] In the present invention, the core layer also includes security governance, which includes metadata management, data desensitization, data privacy and data permissions. The analysis module includes multi-scenario data statistical analysis, multi-industry data statistical analysis, multi-regional data statistical analysis, industrial chain data access analysis, industrial chain operation monitoring, industrial chain effectiveness diagnosis, supply chain element analysis, and industrial chain data application analysis. The data feature layer includes the scenario module, which includes the scenarios in the production and processing process, equipment operation and maintenance, environmental monitoring, and production safety. The support layer includes an identification data element registration and trading platform, and this platform includes data storage, data registration, data trading, and subject management.

[0045] The present invention also proposes an industrial Internet identification data element analysis method, comprising the following steps:

[0046] S1: Establish a support system layer, build blockchain infrastructure and set up an identification data element registration platform. The construction of blockchain infrastructure provides industrial Internet identification data element analysis methods and systems with services based on account maintenance, consensus mechanism, smart contracts and encrypted evidence storage. The blockchain consensus mechanism is used to ensure the reliability of the data element analysis system. The identification data element registration platform includes four main modules: data evidence storage, data registration, data transaction and subject management, to establish the credibility and value of data.

[0047] S2: Building a data feature layer, which includes: adding data identification, data cleaning, and establishing a scenario data model;

[0048] S201: Add data identification, by uniquely identifying and describing various data for cross-industry collaborative applications such as industry data, regional data, industrial chain data, supply chain data, and process data, and by assigning a unique identifier to each data entity, so as to identify and track the data throughout the entire data management and use process;

[0049] S202: Data cleaning: cleaning data through data screening and filtering, data standardization and format unification, missing value processing, outlier processing, data integration, grouping and aggregation;

[0050] S203: Establishing a scenario data model, which includes a production and processing data model, an equipment operation and maintenance data model, an environmental monitoring data model, and a production safety data model;

[0051] S3: Build the core layer, first build the data raw materials, then build the computing engine, then build the data analysis layer, and finally build the intelligent decision-making layer.

[0052] In particular, the production and processing data model mainly defines key data fields such as production procedures, process parameters, and production indicators, including timestamp, product information, production status, and process parameters; the equipment operation and maintenance data model defines key data fields such as equipment information, equipment status, operating parameters, and maintenance records, including equipment ID, equipment type, operating status, and maintenance cycle; the environmental monitoring data model defines key data fields such as environmental monitoring points, environmental parameters, and monitoring time, including indicators of air quality, water quality monitoring, noise monitoring, and temperature and humidity; the production safety data model defines key data fields such as safety incidents, accident records, and safety inspections, including accident type, accident location, accident cause, and safety inspection results.

[0053] Construct data raw materials, i.e., the original data used in the data element analysis system, including data elements, data space, log data, dimensional data, composite data, and real-time data; construct computing engines including three engine computing methods: batch computing engine, streaming computing engine, and batch and stream integrated; when constructing the data analysis layer, create various application scenarios for different industries such as shipping, automobiles, engineering machinery, medicine, food, daily chemicals, and electronic information; develop functions such as production process analysis, quality management and defect analysis, industrial chain operation monitoring, industrial chain effectiveness diagnosis, and supply chain element analysis; construct an intelligent decision-making layer to build a SaaS application system for industrial chain operation monitoring, industrial chain map, industrial public opinion capture, industrial early warning analysis, and industrial decision-making assistance.

[0054] In the description of this article, it should be noted that relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

Claims

1. An industrial Internet identification data element analysis system, comprising a core layer, characterized in that: The core layer is connected to the analysis layer, the intelligent decision-making layer, the data feature layer, and the support layer. The core layer includes a platform interface, a computing engine, and data raw materials. The platform interface includes an HTTP or REST interface, a TCP or Socket or RPC interface, a JSON or XML interface. The computing engine includes a batch computing engine, a streaming computing engine, and a streaming-batch integrated engine. The data raw materials include data elements, data space, log data, dimensional data, aggregated data, and factual data. The intelligent decision-making layer includes industrial chain operation monitoring, industrial chain map, industrial public opinion capture, industrial early warning analysis, and industrial decision-making assistance. The analysis layer includes subject management and analysis modules. Subject management includes data resource holders, data processing users, and data product operators. The data feature layer includes industries, regions, industrial chains, supply chains, and processes. The support layer includes blockchain infrastructure, and the blockchain infrastructure includes account book maintenance, consensus mechanism, smart contracts, and evidence storage services.

2. The industrial Internet identification data element analysis system according to claim 1 is characterized in that: The core layer also includes security governance, which includes metadata management, data desensitization, data privacy and data permissions.

3. The industrial Internet identification data element analysis system according to claim 1 is characterized in that: The analysis modules include multi-scenario data statistical analysis, multi-industry data statistical analysis, multi-regional data statistical analysis, industrial chain data access analysis, industrial chain operation monitoring, industrial chain effectiveness diagnosis, supply chain element analysis, and industrial chain data application analysis.

4. The industrial Internet identification data element analysis system according to claim 1 is characterized in that: The data feature layer includes scenario modules, which include scenarios in production and processing, equipment operation and maintenance, environmental monitoring, and production safety.

5. The industrial Internet identification data element analysis system according to claim 1 is characterized in that: The support layer includes an identification data element registration and transaction platform, and this platform includes data storage, data registration, data transaction and subject management.

6. A method for analyzing industrial Internet identification data elements, characterized in that: The following steps are involved: S1: Establish a support system layer, build blockchain infrastructure and set up an identification data element registration platform. The construction of blockchain infrastructure provides industrial Internet identification data element analysis methods and systems based on account maintenance, consensus mechanism, smart contracts and encrypted evidence services. The identification data element registration platform includes four main modules: data evidence, data registration, data transaction and subject management, to establish the credibility and value of data; S2: Building a data feature layer, which includes: adding data identification, data cleaning, and establishing a scenario data model; S201: Add data identification, by uniquely identifying and describing various data for cross-industry collaborative applications such as industry data, regional data, industrial chain data, supply chain data, and process data, and by assigning a unique identifier to each data entity, so as to identify and track the data throughout the entire data management and use process; S202: Data cleaning: cleaning data through data screening and filtering, data standardization and format unification, missing value processing, outlier processing, data integration, grouping and aggregation; S203: Establishing a scenario data model, which includes a production and processing data model, an equipment operation and maintenance data model, an environmental monitoring data model, and a production safety data model; S3: Build the core layer, first build the data raw materials, then build the computing engine, then build the data analysis layer, and finally build the intelligent decision-making layer.

7. The method for analyzing industrial Internet identification data elements according to claim 6, characterized in that: The production and processing process data model mainly defines key data fields such as production procedures, process parameters, production indicators, including timestamp, product information, production status, and process parameters; the equipment operation and maintenance data model defines key data fields such as equipment information, equipment status, operating parameters, and maintenance records, including equipment ID, equipment type, operating status, and maintenance cycle; the environmental monitoring data model defines key data fields such as environmental monitoring points, environmental parameters, and monitoring time, including indicators of air quality, water quality monitoring, noise monitoring, and temperature and humidity; the production safety data model defines key data fields such as safety incidents, accident records, and safety inspections, including accident type, accident location, accident cause, and safety inspection results.

8. The method for analyzing industrial Internet identification data elements according to claim 6, characterized in that: The data raw materials for construction are the original data used in the data element analysis system, including data elements, data space, log data, dimensional data, composite data and real-time data; the calculation engine for construction includes three engine calculation methods: batch calculation engine, streaming calculation engine and integrated streaming and batch calculation.

9. The method for analyzing industrial Internet identification data elements according to claim 6, characterized in that: When constructing the data analysis layer, various application scenarios are created for different industries such as ships, automobiles, engineering machinery, medicine, food, daily chemicals, and electronic information, and functions of production process analysis, quality management and defect analysis, industrial chain operation monitoring, industrial chain effectiveness diagnosis, and supply chain element analysis are developed. The construction of the intelligent decision-making layer constructs a SaaS application system for industrial chain operation monitoring, industrial chain mapping, industrial public opinion capture, industrial early warning analysis, and industrial decision-making assistance.

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